Preschoolers’ initial planning time on problem-solving performance across task difficulties: insights from intelligent tutoring mobile application log data
Bibliographic record
Abstract
Planning is a critical cognitive ability that emerges during the preschool years and plays a key role in problem-solving. This study investigated the impact of preschool children’s initial planning time on their problem-solving performance across tasks of varying difficulty, using mobile log data from 1,318 children aged 5–6. Three main findings emerged from analyses using linear mixed models (LMM), generalized linear mixed models (GLMM), moderated LMM, and moderated GLMM. Specifically, as task complexity increased, children spent less time on initial planning and made more extra moves, but demonstrated improved accuracy on their first moves. Moreover, longer planning time was consistently associated with fewer extra moves across all task difficulties, highlighting the universal benefits of careful planning. Finally, increased planning time led to higher accuracy on the first move, with the strongest effect observed for harder tasks. However, a ceiling effect was observed, indicating diminishing returns after a certain point. These results could provide nuanced insights into preschool children’s planning and problem-solving processes, particularly under conditions of varying complexity. Findings from this study could have practical implications for early childhood education, offering guidance for designing interventions that foster effective planning and problem-solving skills in young children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".